Artificial neural networks are frequently used for the processing of various real-world stimuli such as, for example, image data, audio data, and/or other types of data (e.g., for pattern recognition). For example, in various embodiments, spiking neural networks and/or traditional Von-Neumann architecture may be utilized for such processing. Artificial neural networks typically include many neurons (or synapses) that are connected in various arrangements to one another and have weights assigned to each of the connections (e.g., denoting the significance of the connection). In many systems, each of those connection weights is stored separately in a synaptic weight memory. As such, neural networks with sparse connectivity often store many zero values in the synaptic weight memory.